3D pavement data decomposition and texture level evaluation based on step extraction and Pavement-Transformer

Pavement texture evaluation is important for driving both skid resistance and pavement maintenance. Limited by the requirements of automation, efficiency and data coverage requirements, most pavement methods focus on damaged areas and static measurement environment. However, maintenance work is practically performed on the entire pavement rather than only the damaged areas, thus a gap between theory and practice is observed. In this study, we designed an efficient texture decomposition method based on the proposed step signal extraction algorithm, which can overcome road fluctuations and accurately extract pavement texture. The Pavement Transformer is introduced for fine texture evaluation, and can better serve pavement maintenance in practice. We conducted experiments on 22,800 pieces of 3D laser scanning data. The results demonstrate that our decomposition method has improved accuracy and stability. Moreover, the classification accuracy of texture level evaluation is 95.2%, which is better than that of the Vision Transformer.